This section provides background information related to the present disclosure which is not necessarily prior art.
Feature extraction provides the foundation for many image processing tasks, for example, image classification and object recognition. Traditional feature extraction techniques can be classified as a model-based approach, a data-driven approach or some combination of a model-based and data-driven approach. Model-based approaches of feature extraction typically utilize rules, obtained from training images, to map features to objects in unknown images. Data-driven approaches of feature extraction typically utilize a relatively large number of training images from which to derive feature-to-object mapping examples. Combination or “hybrid” approaches utilize a combination of rules and feature-to-object mapping examples.
The present disclosure provides for a feature extraction technique that utilizes both model-based and data-driven approaches to obtain improved performance, for example, in image classification and object recognition.